Domain adaptation and compensation for emotion detection

Abstract

Inspired by the recent improvements in domain adapta-tion and session variability compensation techniques used for speech and speaker processing, we study their effect for emo-tion prediction. More specifically, we investigated the use of publicly available out-of-domain data with emotion annotations for improving the performance of the in-domain model trained using 911 emergency-hotline calls. Following the emotion de-tection literature, we use prosodic (pitch, energy, and speaking rate) features as the inputs to a discriminative classifier. We performed segment-level n-fold cross validation emotion pre-diction experiments. Our results indicate significant improve-ment of performance for emotion prediction exploiting out-of-domain data. Index Terms: emotion detection, domain adaptation 1.